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Single-round evolution of RNA aptamers with GRAPE-LM

Nature Biotechnology (2026)Cite this article

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Abstract

The directed evolution of biomolecules is an iterative process. Although advancements in language models have expedited protein evolution, effectively evolving RNA remains a challenge. RNA aptamers, selected for their binding properties, provide an ideal system to address this challenge, yet traditional aptamer discovery still relies on labor-intensive, multi-round screening. Here we introduce GRAPE-LM (generator of RNA aptamers powered by activity-guided evolution and language model), a generative artificial intelligence framework designed for the one-round evolution of RNA aptamers. GRAPE-LM integrates a transformer-based conditional autoencoder with nucleic acid language models and is guided by CRISPR−Cas-based aptamer screening data derived from intracellular environments. We validate GRAPE-LM on three disparate targets: the human T cell receptor CD3ε, the receptor-binding domain of the SARS-CoV-2 spike protein and the human oncogenic transcription factor c-Myc (an intracellular disordered protein). GRAPE-LM, informed with only a single round of CRISPR−Cas-based screening, successfully obtains RNA aptamers that outperform those driven from multiple rounds of human selection and optimization.

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Fig. 1: Illustration of GRAPE-LM and computational results.
Fig. 2: Experimental validation of RNA aptamer evolution with GRAPE-LM.
Fig. 3: Benchmarking GRAPE-LM-derived aptamer leads targeting CD3ε.
Fig. 4: Validation of GRAPE-LM-derived aptamer leads targeting RBD.
Fig. 5: Evaluations of GRAPE-LM on human c-Myc.

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Data availability

The CRISmers sequencing data from both primary screen and pooled examination are available from Zenodo: https://zenodo.org/records/18050896 (ref. 11) and https://doi.org/10.5281/zenodo.18005327 (ref. 79). The minimum datasets used and model checkpoints for GRAPE-LM are available from GitHub: https://github.com/tansaox2008123/GRAPE-LM. Source data are provided with this paper.

Code availability

The source code of GRAPE-LM is available at https://github.com/tansaox2008123/GRAPE-LM. Through the GRAPE-LM online platform (https://grape-lm.bioailab.net/), researchers can easily retrieve aptamer sequences designed for three specific molecular targets featured in this publication.

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Acknowledgements

We thank all colleagues in our laboratories for experimental assistance and helpful discussions. This work was supported by the National Key Research and Development Program of China (no. 2023YFA0915000 to Y.W.), the National Natural Science Foundation of China (no. 91957121 and no. 82273967 to Y.W., no. 82273890 to Y.Z. and no. 62302311 to Jun Zhang), the Department of Science and Technology of Guangdong Province (no. 2021QN020576 to Y.W.), the Guangdong Basic and Applied Basic Research Foundation (no. 2024A1515011681 to Jun Zhang), the Shenzhen Science and Technology Program (no. ZDSYS20220303153551001 to Y.W.), the Shenzhen Stable Support Grant (no. GXWD 20231130103401001 to Y.Z.), the Shenzhen Science and Technology Program (no. JCYJ20240813104817024 to Y.Z.), the China Postdoctoral Science Foundation (no. 2023M742397 and no. 2024T170585 to Ju Zhang), the Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation (no. GZC20231724 to Ju Zhang) and the Internal Fund of the National Engineering Laboratory for Big Data System Computing Technology (no. SZU-BDSC-IF2024-01 to Jun Zhang).

Author information

Author notes

  1. These authors contributed equally: Jun Zhang, Ju Zhang.

  2. These authors jointly supervised this work: Yu Wang, Yang Zhang.

Authors and Affiliations

  1. School of Artificial Intelligence, Shenzhen University, Shenzhen, China

    Jun Zhang  (张军), Shaoxuan Tang  (唐少轩) & Hao Zeng  (曾浩)

  2. National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China

    Jun Zhang  (张军), Shaoxuan Tang  (唐少轩) & Hao Zeng  (曾浩)

  3. State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Ju Zhang  (张菊), Chuancheng Liu  (刘传承), Yushan Cai  (蔡雨珊) & Yu Wang  (王宇)

  4. College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China

    Ju Zhang  (张菊), Chuancheng Liu  (刘传承), Yushan Cai  (蔡雨珊), Bei Liu  (柳贝) & Yu Wang  (王宇)

  5. School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China

    Xiangjie Meng  (孟翔杰) & Yang Zhang  (张阳)

  6. School of Biomedical Engineering, Harbin Institute of Technology, Shenzhen, China

    Xiangjie Meng  (孟翔杰) & Yang Zhang  (张阳)

  7. Shenzhen Loop Area Institute, Shenzhen, China

    Yang Zhang  (张阳)

  8. Shenzhen Key Laboratory of Genome Manipulation and Biosynthesis, Shenzhen, China

    Yu Wang  (王宇)

  9. Shanghai Key Laboratory of Rare Disease Gene Editing and Cell Therapy, Fudan University, Shanghai, China

    Yu Wang  (王宇)

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  1. Jun Zhang  (张军)
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Contributions

Y.W., Y.Z. and Jun Zhang conceived and designed the study. Jun Zhang, S.T., H.Z. and X.M. performed modeling and computational analyses. Ju Zhang, C.L., Y.C. and B.L. performed wet lab experiments. All authors analyzed the data and interpreted the results. Y.W., Y.Z., Jun Zhang and Ju Zhang wrote the manuscript, with contributions from all coauthors. Jun Zhang and Ju Zhang contributed equally to this work. Y.W. and Y.Z. supervised the project. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jun Zhang  (张军), Yang Zhang  (张阳) or Yu Wang  (王宇).

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Nature Biotechnology thanks Pranam Chatterjee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of two latent spaces and data processing.

(a) Comparison of activity-guided versus sequence similarity-based semantic spaces. The activity-guided latent space (constrained by pseudo-activity scores) enables focused sampling of functional aptamers, while traditional sequence similarity-based spaces permit only random exploration. (b) Data processing pipeline. Training and test datasets were derived from the first-round output of CRISmers (an intracellular CRISPR/Cas-based aptamer screening system). Unique sequences were assigned pseudo-activity scores (0-1 scale) based on enrichment frequencies. \(\lambda\) is a hyperparameter that needs to be optimized for different targets, and it is recommended to initially use 0.05. The sequences were clustered using CD-HIT with a threshold of 0.8. Sequences only with a high confidence were used (clusters with more than 10 reads or individual unique sequences with more than 5 reads). For targets (such as RBD) with fewer initial sequences, the threshold of individual unique sequences was set to 1. The final selected sequences were then randomly divided into training and test groups in an 8:2 ratio to construct the training and test sets. Icons in figures were created with BioRender (https://www.biorender.com/).

Extended Data Fig. 2 Exploring the role of activity guidance strategy.

To evaluate the regulatory role of the activity-guidance module on latent space organization, we analyzed how varying the activity-guidance loss weight influenced the separation between high-ranking and low-ranking activity samples. Using t-SNE, we mapped the latent features generated by GRAPE-LM’s encoder of 2,000 samples per group from high-dimensional to 2D space, facilitating cluster analysis of generated aptamers based on functional similarity.

Extended Data Fig. 3 Comparative results of GRAPE-LM and RaptGen in terms of the recovery rate.

The overall recovery rates are calculated using the corresponding model on the test sets of four targets from CRISmers (a) and SELEX (b). Data plot mean with standard deviation (SD), n = 3 experimental replicates. The statistical analyses were conducted using a two-sided Student’s t-test (**** P < 0.0001).

Source data

Extended Data Fig. 4 Dose-dependent flow cytometry analyses of the Cy5-labeled CD3ε aptamers.

(a) Representative flow cytometry histograms showing fluorescence intensity shifts in CD3ε-Ko #2 cell after treatment with serially diluted Cy5-aptamers. (b) Dose-response binding curves of the Cy5-labeled CD3ε aptamers on Jurkat cells. CD3ε knock-out cells (Jurkat CD3ε-Ko #1 and #2) serve as negative control to assess binding specificity. Apparent equilibrium dissociation constants (Kd) were derived from nonlinear regression analysis (one-site specific binding model) using GraphPad Prism. Data represent mean ± SD (n = 3 biological replicates).

Source data

Extended Data Fig. 5 Characterization of aptamer binding affinity for CD3ε.

(a) Luciferase reporter assay results of the top 50 candidate sequences from one round of CRISmers screening, ranked by sequencing read abundance. Data represent mean with SD, n = 3 biological replicates. (b) Comparative GFP reporter assay of libraries from one-round screening using CRISmers versus GRAPE-LM. “Mock” denotes transfection with unrelated control plasmids; “CRISmers” represents transfection with the sub-library constructed after one round of CRISmers screening; “GRAPE-LM” corresponds to transfection with the library generated by GRAPE-LM in a single round.

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Extended Data Fig. 6 The Microscale Thermophoresis examination and the additional results for RBD.

(a) The schematic diagram of the Microscale Thermophoresis (MST). Icons in figures were created with BioRender (https://www.biorender.com/). (b) MST binding curves of previously reported SELEX-derived aptamers targeting the RBD of SARS-CoV-2 Spike protein. Data showed mean with SD, with n = 3 biological replicates. (c) Results of Kd value determination based on MST for the Lead of 2nd round and the Lead of 5th round from iterative CRISmers. Data showed mean with SD, with n = 3 biological replicates. The receptor-binding domain (RBD) resides in the S1 subunit of the SARS-CoV-2 Spike glycoprotein. To validate binding specificity during MST assays, the S2 subunit—a non-target structural region—was used as a negative control, ensuring the aptamer selectively recognizes the RBD rather than unrelated epitopes.

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Extended Data Fig. 7 Case study of the internal loop motif in two representative aptamers.

Prediction of secondary structures and functional analysis of predicted binding sites for representative CD3ε (a) and RBD (b) aptamers. The dashed box highlights the computationally predicted binding site within the internal loop. Data represent the mean with SD (n = 3 biological replicates).

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Extended Data Fig. 8 A powerful new paradigm of accelerated RNA aptamer evolution.

This new paradigm is enabled by one shot GRAPE-LM introduced in this work, informed by one round CRISmers. Icons in figures were created with BioRender (https://www.biorender.com/).

Supplementary information

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Supplementary Figs. 1−12, Supplementary Tables 1−4 and Supplementary Notes 1−3.

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Zhang, J., Zhang, J., Tang, S. et al. Single-round evolution of RNA aptamers with GRAPE-LM. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03007-5

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